Evidence map›Paper›PMID 40603317›Full record

ArticleNature communications2025

Resolving hyperelasticity-adhesiveness conflict in polymer networks by in situ constructing mechanical heterogeneities.

Ping Zhang, Haowei Ruan, Qingxian Li, Yunfeng He, Canhui Yang

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ping Zhang *Shenzhen Key Laboratory of Soft Mechanics & Smart Manufacturing, Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, China.
Haowei Ruan *Shenzhen Key Laboratory of Soft Mechanics & Smart Manufacturing, Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, China.ORCID http://orcid.org/0009-0006-9733-266X
Qingxian LiShenzhen Key Laboratory of Soft Mechanics & Smart Manufacturing, Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, China.
Yunfeng HeShenzhen Key Laboratory of Soft Mechanics & Smart Manufacturing, Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, China.ORCID http://orcid.org/0000-0001-6810-8630
Canhui YangShenzhen Key Laboratory of Soft Mechanics & Smart Manufacturing, Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, China. yangch@sustech.edu.cn.ORCID http://orcid.org/0000-0001-5674-834X

Funding

National Natural Science Foundation of China (National Science Foundation of China) 12302212
6 · The paper itself

Abstract

Stretchable materials with low hysteresis and strong adhesion are needed in applications, but unifying the two contradictory mechanical properties is challenging. Herein, we propose the design principles of polymer networks that are hyperelastic yet adhesive by rationalizing mechanical heterogeneities. The heterogeneous networks comprise a viscoelastic adhesive surface and a hyperelastic non-adhesive bulk. The former has a stiffness much smaller than that of the latter. We synthesize the networks by harnessing the oxygen inhibition mechanism, construct a polymerization phase diagram by reconciling the kinetics of polymerization and radical quenching of oxygen, and establish a power law criterion for the transition from a viscoelastic adhesive network to a hyperelastic adhesive network. We illustrate the principle with heterogeneous poly(butyl acrylate-co-acrylic acid) networks, achieving hysteresis <5% and adhesion energy >300 J/m

Identifiers

PMID40603317
PMCPMC12222520

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.